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Supervised learning of similarity measures for content-based 3D model retrieval

Laga, H.ORCID: 0000-0002-4758-7510 and Nakajima, M. (2008) Supervised learning of similarity measures for content-based 3D model retrieval. In: Tkunaga, T. and Ortega, A., (eds.) Large-Scale Knowledge Resources. Construction and Application. Springer Berlin Heidelberg, pp. 210-225.

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In this paper we investigate on how the choice of similarity measures affects the performance of content-based 3D model retrieval (CB3DR) algorithms. In CB3DR, shape descriptors are used to provide a numerical representation of the salient features of the data, while similarity functions capture the high level semantic concepts. In the first part of the paper, we demonstrate experimentally that the Euclidean distance is not the optimal similarity function for 3D model classification and retrieval. Then, in the second part, we propose to use a supervised learning approach for automatic selection of the optimal similarity measure that achieves the best performance. Our experiments using the Princeton Shape Benchmark (PSB) show significant improvements in the retrieval performance.

Item Type: Book Chapter
Publisher: Springer Berlin Heidelberg
Copyright: 2008 Springer-Verlag Berlin Heidelberg
Other Information: Third International Conference on Large-Scale Knowledge Resources, LKR 2008, Tokyo, Japan, March 3-5, 2008. Proceedings Lecture Notes in Computer Science; Vol. 4938
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